PhD student at University of Toronto, supervised by Steven Waslander. We're interested in perception and decision-making algorithms that can adapt to open-world settings.
Letian Wang
Hi there! I'm a Ph.D. student at the University of Toronto, where I am so fortunate to be supervised by the brilliant Prof. Steven Waslander (Nicest Steve Ever!) in the Toronto Robotics and AI Lab. I'm affiliated with Vector Institute founded by Prof. Geoffrey Hinton. Previously, I was also fortunate to do research with/at:
My research interests lie in the intersection between autonomous driving, robotics, machine learning, computer vision, with special interest in 3D vision, multimodal agent, end-to-end driving, human-robot interaction, and behavior forecasting. I recently focus on developping generalizable decision-making and scalable perception systems, powered by foundation models and learning paradigm that scales well with data, viewing safety as the most precious priority.
I have authored 1 book, was the winner of 2022 CARLA autonomous driving challenge, and won the best paper award honorable mention at RA-L 2021, first prize in the National Challenge Cup 2017 (全国挑战杯一等奖, known as the Olympics of sci./tech. for university students in China), and co-founded a start-up in industrial UAVs.
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I am always happy to chat or collaborate with people with different backgrounds. If you are interested in my work, please feel free to reach out!
I'm looking for internship in 2025, and exploring post-doc, industrial positions, and academic position for 2026.
letianwang0 at gmail dot com

2025
Three proposals selected as Qualcom Fellowship Finalist2018
May-Fourth Medal, highest honor for undergraduate at Beihang university, 10 people each year2017
First prize in National Challenge Cup (全国挑战杯一等奖, known as Sci./Tech. Olympics among universities in China)

Research intern at the Autonomous Vehicle Group of NVIDIA Research, with Peter Karkus, Seung Wook Kim, Boris Ivanovic, Yue Wang, Sanja Fidler, and Marco Pavone. I worked on self-supervised representation learning via generalizable neural radiance field, toward exploring potential foundation model for autonomous driving [NeurIPS'24].

Research assistant at the ICL Lab, with Changliu Liu and Yeping Hu, at the Robotics Institute of Carnegie Mellon University. I worked on generalizable motion prediction algorithms in different scenarios, and social interaction for autonomous driving [NeurIPS'21, AAAI'22, Book]

Research assistant at the MSC Lab, with Liting Sun, Wei Zhan, and Masayoshi Tomizuka, at UC Berkeley. I worked on socially-compatible behavior generation for autonomous driving [RA-L'21] (Best Paper Award Honorable Mention)

SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction
Yang Zhou*, Hao Shao*, Letian Wang*, Steven L. Waslander, Hongsheng Li, Yu Liu
International Conference on Learning Representations (ICLR 2025)
Exploring the Scaling Laws in Motion Prediction!
PDF •
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features
Letian Wang, Seung Wook Kim, Jiawei Yang, Cunjun Yu,
Boris Ivanovic, Steven L Waslander, Yue Wang, Sanja Fidler, Marco Pavone, Peter Karkus
Advances in Neural Information Processing Systems (NeurIPS 2024)
Webpage •
PDF •
Video

Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning
Hao Shao, Shengju Qian, Han Xiao, Guanglu Song, Zhuofan Zong, Letian Wang, Yu Liu, Hongsheng Li
Advances in Neural Information Processing Systems (NeurIPS 2024, Spotlight)
PDF •

SmartRefine: An Scenario-Adaptive Refinement Framework for Efficient Motion Prediction
Yang Zhou, Hao Shao, Letian Wang, Steven L Waslander,
Hongsheng Li, Yu Liu
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)
Outperform all published ensemble-free works on Argoverse 2 leaderboard (single
agent track).
PDF •
Code
LmDrive: Closed-Loop End-to-End Driving with Large Language Models
Hao Shao, Yuxuan Hu, Letian Wang, Steven L Waslander,
Yu Liu, Hongsheng Li
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)
First work to bring LLM into closed-loop end-to-end autonomous
driving.
Webpage •
PDF •
Code

Accelerating Reinforcement Learning for Autonomous Driving using Task-Agnostic and Ego-Centric Motion Skills
Tong Zhou*, Letian Wang*, Ruobing Chen, Wenshuo Wang,
Yu Liu
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
PDF •
Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors
Letian Wang, Jie Liu, Hao Shao, Wenshuo Wang, Ruobing
Chen, Yu Liu, Steven L Waslander
Robotics: Science and Systems (RSS 2023)
PDF •
Code
Talk

ReasonNet: End-to-End Driving with Temporal and Global Reasoning
Hao Shao, Letian Wang, Ruobing Chen, Steven L
Waslander, Hongsheng Li, Yu Liu
Conference on Computer Vision and Pattern Recognition (CVPR 2023)
Winner of CARLA Autonomous Driving Challenge 2022
PDF •
Code

Social Interactions for Autonomous Driving: A Review and Perspectives
Wenshuo Wang, Letian Wang, Chengyuan Zhang, Changliu
Liu, Lijun Sun
Foundation and Trends in Robotics (Book)
PDF •
Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer
Hao Shao*, Letian Wang*, Ruobing Chen, Hongsheng Li,
Yu Liu
Conference on Robot Learning 2022
First Place on the CARLA Leaderboard (Sensor Track)
PDF •
Code

Efficient Game-Theoretic Planning with Prediction Heuristic for Socially-Compliant Autonomous Driving
Chenran Li, Tu Trinh, Letian Wang, Changliu Liu,
Masayoshi Tomizuka, Wei Zhan
IEEE Robotics and Automation Letters 2021
PDF •

Human Instruction Following: Graph Neural Network Guided Object Navigation
Hongyi Chen, Letian Wang, Yuhang Yao, Ye Zhao,
Patricio Vela
The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022, Workshop on Embodied
AI
PDF •

Transferable and Adaptable Driving Behavior Prediction
Letian Wang, Yeping Hu, Liting Sun, Wei Zhan,
Masayoshi Tomizuka, Changliu Liu
arxiv
PDF •

Online Adaptation of Neural Network Models by Modified Extended Kalman Filter for Customizable and Transferable Driving Behavior Prediction
Letian Wang, Yeping Hu, Changliu Liu
AAAI Conference on Artificial Intelligence, Workshop on Human-Centric Self-Supervised Learning
PDF •
Hierarchical Adaptable and Transferable Networks (HATN) for Driving Behavior Prediction
Letian Wang, Yeping Hu, Liting Sun, Wei Zhan,
Masayoshi Tomizuka, Changliu Liu
Conference on Neural Information Processing Systems (NeurIPS 2021), Workshop on Machine Learning for
Autonomous Driving (Spotlight)
PDF •
Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data
Letian Wang, Liting Sun, Masayoshi Tomizuka, Wei
Zhan
IEEE Robotics and Automation Letters 2021 Best Paper Award - Honorable
Mention
PDF •
Overall Design and Control of Coaxial Tilt Rotor Vertically Take-off-and-Landing UAV
Letian Wang, Yuhan Lu, Yibo Liu, Yicong Fu, Bonan Xu, Jingyu Zhao, Qi Qian, Yifan Yan, Weijun Wang
First prize in National Challenge Cup 2017 (全国挑战杯一等奖, known as the Sci./Tech. Olympics among universities in China).
Starting point for our UAV start-up journey for the later 2 years
2024
Toronto TechTalk2023
IV Workshop on Social Behavior for Autonomous Vehicle2021
NeurIPS Workshop on Machine Learning for Autonomous Driving2020
INFORMS Annual Meeting2024
Co-organizer, the 2nd International Workshop on Socially Interactive Autonomous Mobility (SIAM) at IV‘242023
Program Commitee, Machine Learning for Autonomous Driving Symposium at NeurIPS'232022
Program Commitee, Workshop on Learning for Autonomous Driving at NeurIPS'222020+
Reviewer: IJRR, RSS, NeurIPS, CVPR, ICRA, IROS, ML4AD, TNNLS, TVT, ITS, IV